VS265: Reading Fall2012: Difference between revisions
From RedwoodCenter
Jump to navigationJump to search
(→05 Sep) |
No edit summary |
||
| Line 21: | Line 21: | ||
* [http://redwood.berkeley.edu/vs265/linear-algebra/linear-algebra.html Linear algebra primer] | * [http://redwood.berkeley.edu/vs265/linear-algebra/linear-algebra.html Linear algebra primer] | ||
* [http://redwood.berkeley.edu/vs265/superlearn_handout1.pdf Handout] on supervised learning in single-stage feedforward networks | * [http://redwood.berkeley.edu/vs265/superlearn_handout1.pdf Handout] on supervised learning in single-stage feedforward networks | ||
==== 17 Sep ==== | |||
* [http://redwood.berkeley.edu/vs265/superlearn_handout2.pdf Handout] on supervised learning in multi-layer feedforward networks - "backpropagation" | |||
* Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) [http://redwood.berkeley.edu/vs265/lecun-98b.pdf "Efficient BackProp,"] in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.). | |||
* [http://cnl.salk.edu/Research/ParallelNetsPronounce/ NetTalk demo] | |||
Revision as of 15:55, 17 September 2012
27 Aug
- Dreyfus, H.L. and Dreyfus, S.E. Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint. Daedalus, Winter 1988.
- Bell, A.J. Levels and loops: the future of artificial intelligence and neuroscience. Phil Trans: Bio Sci. 354:2013--2020 (1999) here or here
- 1973 Lighthill debate on future of AI
29 Aug
- Mead, C. Chapter 1: Introduction and Chapter 4: Neurons from Analog VLSI and Neural Systems, Addison-Wesley, 1989.
- Linear neuron models
- Linear time-invariant systems and convolution
- Simulating differential equations
- Dynamics
- Carandini M, Heeger D (1994) Summation and division by neurons in primate visual cortex. Science, 264: 1333-1336.
Optional:
- Land, MF and Fernald, RD. The Evolution of Eyes, Ann Revs Neuro, 1992.
- Zhang K, Sejnowski TJ (2000) A universal scaling law between gray matter and white matter of cerebral cortex. PNAS, 97: 5621–5626.
05 Sep
- Jordan, M.I. An Introduction to Linear Algebra in Parallel Distributed Processing in McClelland and Rumelhart, Parallel Distributed Processing, MIT Press, 1985.
- Linear neuron models
- Linear algebra primer
- Handout on supervised learning in single-stage feedforward networks
17 Sep
- Handout on supervised learning in multi-layer feedforward networks - "backpropagation"
- Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) "Efficient BackProp," in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.).
- NetTalk demo
